Bayesian modeling of location, scale, and shape parameters in skew‐normal regression models. (9th September 2021)

• Record Type: Journal Article
• Authors: Corrales, Martha Lucía; Cepeda‐Cuervo, Edilberto
• Is Part Of: Statistical analysis and data mining. Volume 15:Number 1(2022)
• Page Start: 98
• Page End: 111
• Publication Date: 2021-09-09
• Subjects: Bayesian analysis -- heteroscedasticity -- regression models -- skew‐normal distribution; Data mining -- Statistical methods -- Periodicals; 006.312
• DOI: 10.1002/sam.11548
• Languages: English
• ISSNs: 1932-1864

Abstract

Abstract: In this paper, we propose Bayesian skew‐normal regression models where the location, scale and shape parameters follow (linear or nonlinear) regression structures, and the variable of interest follows the Azzalini skew‐normal distribution. A Bayesian method is developed to fit the proposed models, using working variables to build the kernel transition functions. To illustrate the performance of the proposed Bayesian method and application of the model to analyze statistical data, we present results of simulated studies and of the application to studies of forced displacement in Colombia.